{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(397, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0    70   \n",
       "1  15.0          8         350.0        165    3693          11.5    70   \n",
       "2  18.0          8         318.0        150    3436          11.0    70   \n",
       "3  16.0          8         304.0        150    3433          12.0    70   \n",
       "4  17.0          8         302.0        140    3449          10.5    70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'E:\\programming\\dataset\\Into_to_statstical_learning\\Auto.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "year              int64\n",
       "origin            int64\n",
       "name             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130', '165', '150', '140', '198', '220', '215', '225', '190',\n",
       "       '170', '160', '95', '97', '85', '88', '46', '87', '90', '113',\n",
       "       '200', '210', '193', '?', '100', '105', '175', '153', '180', '110',\n",
       "       '72', '86', '70', '76', '65', '69', '60', '80', '54', '208', '155',\n",
       "       '112', '92', '145', '137', '158', '167', '94', '107', '230', '49',\n",
       "       '75', '91', '122', '67', '83', '78', '52', '61', '93', '148',\n",
       "       '129', '96', '71', '98', '115', '53', '81', '79', '120', '152',\n",
       "       '102', '108', '68', '58', '149', '89', '63', '48', '66', '139',\n",
       "       '103', '125', '133', '138', '135', '142', '77', '62', '132', '84',\n",
       "       '64', '74', '116', '82'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['horsepower'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['horsepower'] = data['horsepower'].replace('?',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['horsepower'] = data['horsepower'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower        int32\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "year              int64\n",
       "origin            int64\n",
       "name             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "n = len(data)\n",
    "for power in range(1,11):\n",
    "    poly = PolynomialFeatures(power,include_bias=False)\n",
    "    X = poly.fit_transform(data['horsepower'].to_frame())\n",
    "    y = data['mpg']\n",
    "    lr = LinearRegression()\n",
    "    scores.append(cross_val_score(lr,X,y,cv = n,scoring = 'neg_mean_squared_error').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = list(map(abs,scores))\n",
    "powers = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(15, 28)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.DataFrame({\"power\":powers,'scores':scores})\n",
    "plt.figure(figsize = (12,6))\n",
    "g = sns.FacetGrid(data = temp,size=5)\n",
    "g.map(plt.scatter, 'power' , 'scores')\n",
    "g.map(plt.plot, 'power', 'scores')\n",
    "plt.title('LOOCV')\n",
    "plt.ylim(15,28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_list = []\n",
    "from sklearn.utils import shuffle\n",
    "for _ in range(10):\n",
    "    scores = []\n",
    "    n = len(data)\n",
    "    for power in range(1,11):\n",
    "        poly = PolynomialFeatures(power,include_bias=False)\n",
    "        X = poly.fit_transform(data['horsepower'].to_frame())\n",
    "        y = data['mpg']\n",
    "        X,y = shuffle(X,y)\n",
    "        lr = LinearRegression()\n",
    "        scores.append(cross_val_score(lr,X,y,cv = 10,scoring = 'neg_mean_squared_error').mean())\n",
    "    scores_list.append(list(map(abs,scores)))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "powers = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {'Scores'+ str(i):scores_list[i] for i in range(10)}\n",
    "temp = pd.DataFrame(data_dict)\n",
    "temp.index = powers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scores0</th>\n",
       "      <th>Scores1</th>\n",
       "      <th>Scores2</th>\n",
       "      <th>Scores3</th>\n",
       "      <th>Scores4</th>\n",
       "      <th>Scores5</th>\n",
       "      <th>Scores6</th>\n",
       "      <th>Scores7</th>\n",
       "      <th>Scores8</th>\n",
       "      <th>Scores9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.203264</td>\n",
       "      <td>24.157633</td>\n",
       "      <td>24.209870</td>\n",
       "      <td>24.231662</td>\n",
       "      <td>24.259720</td>\n",
       "      <td>24.151541</td>\n",
       "      <td>24.128923</td>\n",
       "      <td>24.357917</td>\n",
       "      <td>24.264865</td>\n",
       "      <td>24.147533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.288139</td>\n",
       "      <td>19.354763</td>\n",
       "      <td>19.319733</td>\n",
       "      <td>19.445964</td>\n",
       "      <td>19.099661</td>\n",
       "      <td>19.230037</td>\n",
       "      <td>19.221715</td>\n",
       "      <td>19.338735</td>\n",
       "      <td>19.238891</td>\n",
       "      <td>19.170679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.303618</td>\n",
       "      <td>19.517461</td>\n",
       "      <td>19.423642</td>\n",
       "      <td>19.482946</td>\n",
       "      <td>19.445683</td>\n",
       "      <td>19.320762</td>\n",
       "      <td>19.253842</td>\n",
       "      <td>19.332350</td>\n",
       "      <td>19.211552</td>\n",
       "      <td>19.535712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.644022</td>\n",
       "      <td>19.483272</td>\n",
       "      <td>19.207402</td>\n",
       "      <td>19.241446</td>\n",
       "      <td>19.248328</td>\n",
       "      <td>19.534379</td>\n",
       "      <td>19.438380</td>\n",
       "      <td>19.550736</td>\n",
       "      <td>19.550026</td>\n",
       "      <td>19.442406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.941690</td>\n",
       "      <td>19.215959</td>\n",
       "      <td>19.221399</td>\n",
       "      <td>18.806800</td>\n",
       "      <td>18.850921</td>\n",
       "      <td>19.082429</td>\n",
       "      <td>19.288344</td>\n",
       "      <td>19.540824</td>\n",
       "      <td>18.929547</td>\n",
       "      <td>19.089901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Scores0    Scores1    Scores2    Scores3    Scores4    Scores5  \\\n",
       "1  24.203264  24.157633  24.209870  24.231662  24.259720  24.151541   \n",
       "2  19.288139  19.354763  19.319733  19.445964  19.099661  19.230037   \n",
       "3  19.303618  19.517461  19.423642  19.482946  19.445683  19.320762   \n",
       "4  19.644022  19.483272  19.207402  19.241446  19.248328  19.534379   \n",
       "5  18.941690  19.215959  19.221399  18.806800  18.850921  19.082429   \n",
       "\n",
       "     Scores6    Scores7    Scores8    Scores9  \n",
       "1  24.128923  24.357917  24.264865  24.147533  \n",
       "2  19.221715  19.338735  19.238891  19.170679  \n",
       "3  19.253842  19.332350  19.211552  19.535712  \n",
       "4  19.438380  19.550736  19.550026  19.442406  \n",
       "5  19.288344  19.540824  18.929547  19.089901  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.line(figsize = (14,8))\n",
    "plt.ylim(16,28)\n",
    "plt.title('10 Fold CV')\n",
    "plt.xlabel('Degree')\n",
    "plt.ylabel('Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
